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The majority of this book tries to introduce generic data science topics to computer science novices, most of which are irrelevant to Markov models.
the couple chapters that do cover Markov models are superficial.
you'd be better off finding a couple good blog posts than getting this book.
I like the book. But I gave 4 stars for this one I bought because it contains too many types. I have gotten some background in probability so I wished I could guess what their right words should be.
This is a very interesting book about Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian networks. In it I listened a lot of interesting and informative about Foundations of Markov Models, Case Study: Google PageRank and Inference Tasks
The simplest Markov model is the Markov chain. It models the state of a system with a random variable that changes through time.
By listening this book, I was able to learn the foundation of Markov Models, Markov chains, hidden Markov models and many useful information. Great read. easy to understand.